1. Assessing ETA Violations, and Selecting Attainable/Realistic Parameters Causal Effect/Variable Importance Estimation and the Experimental Treatment Assumption

2. The Need for Experimentation Estimation of Variable Importance/Causal Effect requires assumption not needed for prediction “Experimental Treatment Assignment” (ETA) –Must be some variation in treatment variable A within every stratum of confounders W W must not perfectly predict/determine A g(a|W)>0 for all (a,W)

3. ETA Violations Ex #1: Some treatment histories perfectly predict presence of a given mutation Ex #2: Certain types of individuals never receive once daily therapy ETA violations imply lack of identifiability for variable importance/ causal effect –Reliance on extrapolation –Can lead to serious bias in estimates

4. Diagnostic for ETA Bias Inverse Probability of Treatment Weighted (IPTW) estimators rely completely on ETA –Don’t extrapolate, unlike MLE or T-MLE Diagnostic tool available to quantify the extent of bias in IPTW estimators due to ETA violations –Relies on parametric bootstrap Serves to quantify extent to which ψ is not identifiable –i.e. Extent to which T-MLE estimate relies on extrapolation

5. Example: Causal Effect of Adherence Interventions Goal: Estimate the causal effect of a point treatment –Intervention (A) aimed to improve adherence to antiretroviral drugs A 1 =use of a pill box organizer A 2 =use of once daily therapy –Outcome (Y) = % of prescribed doses taken What is the effect of pill boxes/once daily therapy on adherence?

6. Example: Parameter of Interest Confounding by a range of covariates W –Include: past treatment history, clinical characteristics, socioeconomic variables –E.g. crack users may be less likely to use pill boxes and also less likely to adhere Parameter of Interest: –Difference in expected adherence if whole population had used the intervention E(Y 1 ) vs. if whole population had not used it E(Y 0 )

7. Example: Parameter of Interest Parameter of Interest: E(Y 1 )-E(Y 0 ) If assume no unmeasured confounders (W sufficient to control for confounding) Causal Effect is same as W-adjusted Variable Importance E(Y 1 )-E(Y 0 )=E[E(Y|A=1,W)-E(Y|A=0,W)]= ψ –Same advantages to T-MLE

8. Example: Pill Box Adherence Effect estimates suggest pill box organizers improve adherence, but one daily therapy does not However… ETA diagnostic suggests effect of once daily therapy not identifiable from the data –Can’t adjust for confounders that are perfect predictors –Ex. No Latina women received once daily therapy –Ex. Some type of regimens not available as once daily therapy –Can’t conclude no effect for once daily therapy

9. Example: ETA Bias in the Estimated Effect of Adherence Interventions 850%0% higher adherence (95% CI: -6%-14%) Once Daily Therapy 0.01%4% higher adherence (95%CI:1%-7%) Pill Box Organizer Relative ETA Bias in IPTW Estimate T-MLE Effect EstimateIntervention

10. ETA Violations May Be More than a Nuisance Goal: Estimate the causal effect of a longitudinal treatment on outcome Some subjects, over the course of follow-up, develop contraindications to the treatment What is the real parameter of interest? –Randomized trial in which everyone (even those who develop contraindications) forced to comply with longitudinal assigned treatment –Randomized trial in which those who develop contra- indications over the course of the trial not forced to comply

11. Realistic Causal Effect of Point Treatment Intervention ψ = E{E[Y|A=d(1)(W),W]-E[Y|A=d(0)(W),W]} –d(1)(W)= { a=1 if g(1|W)>α; a=0 otherwise} –d(0)(W)= { a=0 if g(0|W)>α; a=1 otherwise} Similar to Intent-to-Treat analysis –Estimates effect of randomly assigning the intervention, –But… accepts that some individuals may not be able to comply with the regimen to which they are assigned Targeted MLE estimators available

12. Simulation Result: Realistic vs. Truncation of Weights for IPTW Data Generation- A is confounded, with ETA violations – W=Uniform(-5,5); p=1/(1+e -1+1.5*W ); A=Binomial(p) –No causal effect of A: Y= 2+4W 2 +N(0,1) –Simulation run 5000 times for each sample size N=50N=100N=250N=500N=1000 Naïve-7.14-7.30-7.36-7.33-7.30 IPTW4.085.282.261.251.23 IPTW MSE755.32029.97077.94826.72590.9 IPTW-Truncated Weight3.925.756.396.846.74 IPTW-Truncated Weight MSE502.30386.25238.07151.6895.02 IPTW-REAL0.240.060.020.000.01 IPTW-REAL MSE12.325.421.910.890.44

13. Example: HIV resistance mutations Goal: Rank a set of genetic mutations based on their importance for determining an outcome –Mutations (A) in the HIV protease enzyme Measured by sequencing –Outcome (Y) = change in viral load 12 weeks after starting new regimen containing saquinavir How important is each mutation for viral resistance to this specific protease inhibitor drug? –Inform genotypic scoring systems

14. Example: Parameter of Interest Need to control for a range of other covariates W –Include: past treatment history, baseline clinical characteristics, non-protease mutations, other drugs in regimen Parameter of Interest: Variable Importance ψ = E[E(Y|A j =1,W)-E(Y|A j =0,W)] –For each protease mutation (indexed by j)

15. Unadjusted estimates

16. Fully adjusted estimates

17. Delta-adjusted estimates

18. Summary Variable Importance/Causal Effect estimates rely on sufficient experimentation (ETA) for identifiability –Diagnostic tool available to diagnose ETA problems Realistic an Delta-Variable Importance/Causal Effect ensures parameter is identifiable, even with ETA violations –Realistic is often parameter of interest –In general, realistic provides test of null hypothesis for standard variable importance/causal effect ETA problems can also be avoided by selecting the appropriate adjustment set data-adaptively.